Statistical Support &Analysis (SS&A) Core The Statistical Support &Analysis (SS&A) Core will promote the theme of the Center for Advancing Longitudinal Drug Abuse Research (CALDAR) by facilitating the use of advanced statistical methods for analysis of longitudinal data. The SS&A Core has the following specific aims: (1) provide statistical consultation to CALDAR researchers on issues related to the appropriate application of advanced statistical methods for longitudinal research on substance abuse and its interplay with HIV risk and infection and drug treatment and other service systems;(2) identify, coordinate, and provide selected training to enhance the statistical and analytic expertise of CALDAR researchers, pilot study recipients, project analysts, statisticians, and trainees;(3) conduct analyses to further overall CALDAR goals and for other specific topics selected by the Research &Methods Support Core;and (4) address emerging issues in the adaptation and application of longitudinal models/methods to specific empirical situations and issues. Core activities will include consultation to CALDAR-affiliated investigators on statistical and data-related issues;workshops and other training and career development activities regarding statistical modeling in longitudinal research;analyses addressing CALDAR priority research topics and emerging statistical applications and issues. Analytic methods will include growth curve approaches (e.g., through generalized linear/non-linear and multilevel models), latent growth mixture models, growth models for multiple dependent variables, Bayesian Markov approaches, and marginal structural models as appropriate to the research questions and data characteristics. Core staff will work with CALDAR and collaborating researchers to identify and refine research questions and hypotheses related to drug use and recovery trajectories, patterns of HIV and other risk behaviors, and their interaction with service systems such as drug treatment, criminal justice, and mental health. Emergent approaches will be examined and applied to capture the complexity of the recovery process. Efforts should lead to improved application of analytic methods, more effective use of research results in explicating the recovery process, and increased research productivity.